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. 2026 Feb 3;12(1):e70220. doi: 10.1002/trc2.70220

Interconnected pathways: Social determinants of health, infections, and Alzheimer's disease and related dementias

Juehyun Shin 1,✉, Lavinia Alberi 2,3, Graciela Muniz‐Terrera 1,3
PMCID: PMC12865320  PMID: 41641365

Abstract

Social determinants of health (SDOH), infections, and Alzheimer's disease and related dementias (ADRD) are interconnected through complex pathways. Existing research establishes associations between SDOH and infections, between SDOH and ADRD, and between infections and ADRD. However, few studies examine how these three domains interact simultaneously. We propose five conceptual models linking these three concepts, including mediations, confounding, and indirect effects through intermediary processes. Empirical testing of these complex relationships requires different analytical strategies and data structures, such as population‐based cohort studies with comprehensive data collection. We examine methodological considerations for population‐based research, particularly four approaches for capturing infection exposures. Self‐reported data are cost‐effective but have limitations of recall bias and lack of pathogen‐specific confirmation. Record linkage provides clinician‐confirmed diagnoses but misses mild infections and is depends on coding accuracy. Laboratory measures offer objective biomarkers but cannot always pinpoint timing and require resource‐intensive collection. Omics technologies enable systematic investigation of infection–brain health links but still have limitations in technical complexity, costs, standardization, and representation of disadvantaged populations. Future work requires integrating diverse data sources and analytical approaches, addressing gaps in historical infection exposure measurement and life‐course SDOH assessment, and ensuring adequate representation of socioeconomically disadvantaged populations.

Highlights

  • Social determinants of health (SDOH), infections, and Alzheimer's disease and related dementias (ADRD) interact through complex, multidirectional pathways.

  • Five conceptual models are proposed to guide integrated SDOH–infection–ADRD research.

  • Four infection ascertainment approaches are compared: self‐report, linkage, labs, omics.

  • Infection measures vary in validity, timing, cost, and equity of access.

  • Integrating data sources improves infection measurement across the life course.

Keywords: Alzheimer's disease and related dementias, conceptual models, dementia risk, epidemiological research, infection, methodological challenges, social determinants of health

1. INTERCONNECTIONS: SOCIAL DETERMINANTS OF HEALTH, INFECTIONS, AND ALZHEIMER'S DISEASE AND RELATED DEMENTIAS

Social determinants of health (SDOH) are defined by the World Health Organization as “the conditions in which people are born, grow, work, live, and age, and the wider set of forces and systems shaping the conditions of daily life”. 1 These encompass non‐medical factors that profoundly influence health outcomes and quality of life across the lifespan.

1.1. SDOH and Infections

Evidence suggests that SDOH are associated with both infections and Alzheimer's disease and related dementias (ADRD). For instance, after the coronavirus disease 2019 (COVID‐19) pandemic, numerous reports documented that not all populations were affected equally 2 , 3 with minority ethnic groups having a higher risk of severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2) infection. Community‐level factors, such as overcrowding and larger household sizes, which are highly prevalent in socioeconomically disadvantaged communities and areas with higher proportions of minority residents, have also been associated with increased COVID‐19 mortality; 4 with additional studies reporting that environmental pollutants, such as ozone levels and the density of urban industry, also contributed to SARS‐CoV‐2 infection rates across different regions and seasons. Similarly, additional studies report on the associations between other infections such as human immunodeficiency virus/acquired immunodefiency syndrome (HIV/AIDS), SARS, and influenza. 5 Overcrowded housing, homelessness, and poor ventilation are key drivers of tuberculosis transmission, especially in urban slums or migrant housing; 6 poor housing, lack of bed nets, inadequate water drainage, and limited access to health services increase malaria risk, 7 whereas lack of safe water, poor sanitation, and poverty are core drivers of intestinal worm infections in low‐income communities. 8

1.2. SDOH and ADRD

A body of literature demonstrates that SDOH play a critical role in ADRD outcomes. For example, the 2024 Lancet Commission report on 14 modifiable risk factors for dementia includes evidence on five SDOH (low education, hearing impairment, air pollution, social isolation, and untreated vision loss) and their impact on Alzheimer's disease (AD). 9 Other research 10 , 11 , 12 also reports associations between various individual, contextual, neighborhood, and structural SDOH related to ADRD risk, its prevalence and incidence, and cognitive function.

1.3. Infections and ADRD

The literature on infections and ADRD is abundant. For instance, Itzhaki 13 reported on the evidence about herpes simplex virus type 1 (HSV1) in AD; Muzambi et al 14 reviewed the literature on common bacterial infections with risk of dementia or cognitive decline and showed that sepsis, pneumonia, urinary tract infection (UTI), and cellulitis increase dementia risk, but failed to identify well powered studies on the association of infections with cognitive decline. In contrast, Peixoto et al 15 showed that patients with Chikungunya virus infection experience long‐term cognitive problems; investigations on common infections and AD risk 16 further report a positive association between meningitis and AD prior to diagnosis, supporting a potential pathogenic link between a history of meningitis and UTIs and AD later in life. Recent quasi‐experimental evidence has strengthened the case for a causal relationship between varicella zoster virus and dementia. Eyting et al 17 found that live‐attenuated herpes zoster (HZ) vaccination reduced the probability of a new dementia diagnosis by 20% over 7 years in Wales, with stronger protective effects observed among women. This finding was independently supported by Pomirchy et al 18 in Australia, who demonstrated that eligibility for HZ vaccination decreased the probability of receiving a new dementia diagnosis by 1.8 percentage points during 7.4 years of follow‐up. Considering that vaccine uptake from birth differs among socially disadvantaged groups in developing countries while an inverse trend is observed in high‐income nations, 19 , 20 and that the largest portion of the population in the developing world is represented by low‐income groups, the risk for chronic sequelae of infection, with increasing life expectancy, could contribute to raising the incidence of age‐related diseases associated with infection including ADRD.

2. RESEARCH GAPS

Existing research typically focuses on the three direct pathways depicted in Figure 1: (A) SDOH influencing infections, (B) SDOH influencing ADRD, and (C) infections influencing ADRD. However, these seemingly simple relationships do not capture other plausible, more complex relationships between these three factors. To address this gap, we propose five conceptual models (Figure 2) illustrating potential relationships among them.

FIGURE 1.

FIGURE 1

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Current understanding of social determinants of health (SDOH), infections (ID), and Alzheimer's disease and related dementias (ADRD) pathways

FIGURE 2.

FIGURE 2

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Directed acyclic graphs depicting various potential relationships between social determinants of health (SDOH), infections (ID), and Alzheimer's disease and related dementias (ADRD)

2.1. Model 1: Full mediation by infections

Model 1 proposes that infections may mediate the relationship between SDOH and ADRD risk. Socioeconomic disadvantage may compromise immune function through chronic stress, malnutrition, environmental pollution, and reduced healthcare access, potentially increasing susceptibility to infections. 21 These infections may trigger neuroinflammation and neurodegeneration through microglial activation, blood–brain barrier disruption, 22 systemic cytokine release, 23 direct pathogen neurotoxicity, 24 , 25 and amyloid‐beta accumulation as an antimicrobial response. 26 For example, inadequate sanitation may increase risk of waterborne infections linked to cognitive impairment, 27 , 28 while food insecurity could compromise immune function, increasing susceptibility to respiratory infections associated with cognitive decline. 29

2.2. Model 2: Additional direct SDOH effects independent of infections

Model 2 proposes that SDOH influence ADRD through direct, non‐infectious mechanisms, independent of infection‐related processes. In this model, infections are not the primary mechanism of interest; the focus is on identifying and testing specific SDOH‐driven mechanisms that affect ADRD risk even in the absence of infection‐mediated effects. Economic instability may limit access to healthcare and health‐promoting resources, delaying diagnosis and treatment while also limiting management of vascular risk factors and other preventable conditions that contribute to cognitive decline. Poor education reduces cognitive reserve. 30 Occupational exposures to neurotoxic substances, physically demanding work, and low job control may increase ADRD risk through direct neurotoxic effects and chronic stress. 31 Family environment factors, including childhood adversity, parental education, and early‐life socioeconomic position, shape brain development and lifelong cognitive trajectories. Neighborhood factors, including unsafe environments, pollution, lack of green space, and low social trust, increase ADRD risk by increasing stress and reducing opportunities for protective activities. 32 In addition, the Area Deprivation Index, a validated measure of the adverse social exposome, is associated with increased ADRD neuropathology. 32 Domestic social and physical interactions, including domestic violence and social isolation, also increase ADRD risk. 12 Additional SDOH‐ADRD pathways likely exist beyond established findings, but understanding of biopsychosocial mechanisms remains limited, with challenges in defining SDOH constructs, measuring life‐course exposures, and comparing studies due to a lack of standardized data elements and validated SDOH screening tools. 11

2.3. Model 3: Partial mediation (direct + indirect effects)

Model 3 proposes that SDOH affect ADRD through both infection‐mediated and non‐infectious pathways, allowing decomposition of total effects into indirect (through infections) and direct components. Low socioeconomic position may lead to employment in higher‐exposure occupations (e.g., frontline jobs requiring close proximity to the public, inability to work remotely, roles with low wages and limited health insurance), increasing infection frequency 33 and contributing to ADRD risk via inflammatory pathways. Simultaneously, these conditions increase ADRD risk through non‐infectious mechanisms, including occupational exposures to neurotoxic substances, physically demanding work, and low job control, via direct neurotoxic effects and chronic stress. 31 This model thus allows researchers to quantify the proportion of the total SDOH–ADRD association attributable to infections versus other direct pathways, providing insight into the relative importance of infection‐mediated processes alongside other mechanisms.

2.4. Model 4: SDOH as confounders

In Model 4, SDOH may confound the infection‐ADRD relationship, independently influencing both and potentially creating spurious associations if inadequately controlled. For instance, individuals in poverty may experience both higher infection rates due to limited sanitation and higher dementia risk due to poor nutrition and limited education. Studies failing to adjust for SDOH may incorrectly attribute increased dementia risk to infections when the underlying cause could be these shared social determinants. 34 , 35 Controlling for SDOH confounding is challenging because SDOH are difficult to measure historically and may lie on the causal pathway rather than serving purely as confounders.

2.5. Model 5: SDOH effects through other processes

Model 5 proposes that SDOH may impact ADRD through intermediary processes, with cognitive reserve as a primary example. Cognitive reserve refers to the brain's ability to maintain function despite pathological damage through neuroplasticity and neurogenesis. 30 , 36 Higher educational attainment and cognitively complex occupations, both heavily influenced by SDOH, build cognitive reserve, enabling individuals to better tolerate Alzheimer's pathology without showing clinical symptoms. 30 , 36 Engagement in intellectual, social, and physical activities throughout life also contributes to cognitive reserve. 36 Other mediating processes may include cardiovascular health and chronic inflammation, both influenced by SDOH through access to healthcare, nutrition, and environmental exposures. 37 , 38 Testing Model 5 requires longitudinal studies capturing dynamics across the long latency between early‐life SDOH exposures and late‐life dementia onset.

These conceptual models demonstrate that the relationships among SDOH, infections, and ADRD extend far beyond simple univariate associations. While each model is theoretically plausible and supported by partial evidence from the literature, they require different analytical strategies and data structures for empirical testing.

3. METHODOLOGICAL CONSIDERATIONS

Existing large databases present valuable opportunities to empirically test many of the proposed models. Examples of large databases include national and international databases including the various international biobanks (such as the UK and French Biobanks, All of Us Research Program, Canadian Partnership for Tomorrow's Health); epidemiological studies that may have collected questionnaire data or bio samples (examples include the Prevent Dementia study, European Prevention of Alzheimer's Dementia Longitudinal Cohort, Medical Research Council 1946 Birth Cohort), or electronic health records. However, there are limitations in these databases that hinder the ability to test complex hypothesized relationships and address other unresolved questions about alternative plausible underlying mechanisms. Next, we discuss some of these opportunities and limitations and suggest approaches that may be used to at least partially overcome the challenges.

3.1. Self‐Reported infection data

Self‐reported data remain a common and practical approach for capturing information on infections in large‐scale population studies. Self‐reports are cost‐effective and can gather information on infections that may not require medical attention and therefore would be missed by clinical records alone. 39 They are especially useful for common, mild, or seasonal infections such as colds, flu‐like illnesses, or childhood diseases where recall may be reasonably reliable. Self‐reports can also provide valuable contextual detail, such as perceived severity, duration, or impact on daily activities, which is often not captured in administrative data. 39 However, reliance on self‐reported infection data has well‐known limitations. Recall bias can lead to underreporting or misclassification, particularly for asymptomatic or mild infections and for events occurring long ago. 40 Misunderstanding medical terms, social desirability, or stigma can further distort reports, especially for sensitive conditions like sexually transmitted infections. 40 Self‐reports also lack pathogen‐specific confirmation and objective timing, which can limit their validity when precise exposure measurement is needed. 40 The timing of exposure to infectious agents is often unknown or inaccurately recorded, adding further complications when questions about duration or timing of exposure are of interest.41 Although from an analytical perspective, the timing of exposure can be considered a censored variable, this depends on the analytical methodology used. Consequently, while self‐reporting offers practical benefits, combining it with clinical data and biomarkers is recommended to strengthen infection ascertainment and reduce misclassification. 40

3.2. Record linkage

Record linkage offers valuable strengths for capturing infection data in population‐based studies. 42 By linking participants to primary care, hospital, or laboratory, and death records, researchers can access clinician‐confirmed diagnoses, objective test results, and mortality outcomes, reducing reliance on potentially biased self‐reports. 42 This approach is particularly useful for identifying serious or medically attended infections, provides precise timing, and allows detection of recurrent events without additional participant burden. 42 In settings with comprehensive health systems or centralized registries, linkage can achieve near‐complete coverage of recorded infections. However, there are important limitations. Mild or self‐managed infections often go unrecorded, leading to underestimation of true infection burden. 43 Data quality depends on coding accuracy and can vary by provider or over time. Differences in healthcare access and care‐seeking behavior may also introduce systematic bias. Furthermore, clinical records often lack pathogen‐specific detail unless supported by laboratory confirmation, and data may not be available in real time. 43 Ethical and legal requirements for consent and privacy add further complexity. Similarly, the lack of historical data also hampers opportunities to answer questions about the life course model that best explains potential associations between infections and later life outcomes. For these reasons, record linkage is most effective when combined with self‐reports and biomarkers to more comprehensively capture infections.

3.3. Laboratory measures and biomarkers

Laboratory measures such as serological testing and pathogen‐specific biomarkers provide a valuable source of objective information on infection history in population‐based studies. 44 , 45 Unlike self‐reports or routine medical records, serology can identify prior or latent infections, including asymptomatic or undiagnosed cases that would otherwise go unnoticed. 44 Biomarker data also allow quantification of immune response (e.g., antibody titers, inflammatory markers) and can help distinguish between recent, chronic, or past exposure. 46 This approach enhances the precision of infection status classification and enables the study of subclinical infections that may have long‐term health effects. Neuroimaging (e.g., magnetic resonance imaging [MRI]) can support infection exposure ascertainment by detecting meningeal enhancement, focal lesions, abscesses, and encephalitis patterns (e.g., on fluid‐attenuated inversion recovery [FLAIR] and diffusion‐weighted imaging) that may be missed in self‐reports or routine medical records. 47 However, laboratory‐based measures also have limitations. Serological tests cannot always pinpoint the exact timing of infection and may vary in sensitivity and specificity. 45 Cross‐reactivity or waning antibodies can lead to misclassification, especially for older adults or infections with multiple strains. 48 Collecting and processing biological samples is resource‐intensive and may limit sample sizes or introduce selection bias if participation differs by health status. Moreover, ethical considerations related to biospecimen storage, consent, and secondary use must be addressed. Despite these challenges, integrating laboratory measures with self‐report and clinical data remains a robust strategy to improve infection ascertainment in large‐scale epidemiological research. 45 The introduction of self‐testing as observed in the COVID‐19 pandemic at affordable prices using intranasal swabs or saliva testing, or emerging urinary self‐tests for urinary tract infections, represents an opportunity for accessible healthcare solutions that if tracked properly using smartphones, could constitute a wealth of information in the upcoming years.

3.4. Omics technologies

The application of omics technologies has enabled more systematic investigation of how infections (or more broadly, microbial exposures) may influence brain health. Omics data present important opportunities for further research in the area. For instance, using meta‐omics (e.g., metagenomics, metatranscriptomics, metaproteomics, metabolomics) to profile the gut microbiota, researchers can characterize both the composition and functional output of microbial communities, including metabolites and bioactive molecules that may modulate neural or immune processes. 49   For instance, a recent study combining gut microbial sequencing, serum metabolomics and neuroimaging found associations between specific gut microbial taxa, circulating metabolites, brain structural changes, and cognitive decline along the continuum from healthy individuals to mild cognitive impairment to AD. 50   Such integrative approaches allow tracing potential “microbiota → metabolite → brain structure/function → cognition” pathways; by doing so, they offer insight into mechanisms that may link infection, immune‐metabolic perturbations, and neurodegeneration. Moreover, omics applied to host tissues (e.g. transcriptome, proteome, metabolome) enables detection of systemic or brain‐specific responses to infection, immune activation, or metabolic stress that might underlie long‐term brain vulnerability. Taken together, omics‐based research provides a powerful framework for exploring how microbial exposures and infections may influence brain health and disease risk, moving beyond descriptive or candidate‐based studies to molecularly grounded, hypothesis‐generating analyses.

However, omics technologies still have limitations. Technical challenges include data heterogeneity, high dimensionality, and reproducibility issues. 51 Multi‐omics integration is methodologically complex, with models often lacking biological alignment and validation. 51 High costs restrict accessibility, particularly in underrepresented populations, exacerbating disparities. 52 Ethical concerns around privacy, consent, and governance require attention. 52 Lack of standardized protocols and longitudinal data limits capturing temporal dynamics of infection‐related brain changes. 52 In the context of studying SDOH, infections, and ADRD, omics approaches present additional methodological challenges, including difficulties in linking molecular signatures across socioeconomic gradients, challenges in retrospectively capturing historical infection exposures through biomarkers, and limited representation of diverse populations most affected by health disparities. 51

4. CONCLUSIONS

Understanding the complex interplay among SDOH, infections, and ADRD requires moving beyond simple models to consider multiple plausible pathways, including mediations, confounding, and indirect effects through intermediary processes. We have proposed five conceptual models that capture these relationships and identified key methodological challenges in testing them. Each data source (self‐reported infection data, record linkage, laboratory biomarkers, and omics technologies) offers distinct advantages but also limitations in accuracy, temporal precision, cost, and generalizability. Future work in this area will require integrating diverse data sources and analytical approaches, addressing critical gaps in historical infection exposure measurement and life‐course SDOH assessment, and ensuring adequate representation of socioeconomically disadvantaged populations.

CONFLICT OF INTEREST STATEMENT

Dr. Shin is an employee of Ohio University Heritage College of Osteopathic Medicine. Dr. Alberi is the founder and CEO of VitalizeDx Eu Personalized Care in Italy and VitalizeDx Eu in Switzerland, and a collaborator of The Alzheimer's Pathobiome Initiative. Dr. Muniz‐Terrera is an employee of Ohio University Heritage College of Osteopathic Medicine and a collaborator of The Alzheimer's Pathobiome Initiative. Author disclosures are available in the Supporting Information. Author disclosures are available in the Supporting Information.

Supporting information

Supporting Information

TRC2-12-e70220-s001.pdf (304.7KB, pdf)

ACKNOWLEDGMENTS

The authors thank the reviewers for their valuable suggestions, which helped improve the quality of this manuscript. There was no funding involved.

Shin J, Alberi L, Muniz‐Terrera G. Interconnected pathways: Social determinants of health, infections, and Alzheimer's disease and related dementias. Alzheimer's Dement. 2026;12:e70220. 10.1002/trc2.70220

REFERENCES

  • 1. World Health Organization . Social determinants of health . May 6, 2025. Accessed November 10, 2025. https://www.who.int/news‐room/fact‐sheets/detail/social‐determinants‐of‐health
  • 2. Marmot M, Allen J. COVID‐19: exposing and amplifying inequalities. J Epidemiol Community Health. 2020;74(9):681‐682. doi: 10.1136/jech-2020-214720 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 3. Niedzwiedz CL, O'Donnell CA, Jani BD, et al. Ethnic and socioeconomic differences in SARS‐CoV‐2 infection: prospective cohort study using UK Biobank. BMC Med. 2020;18(1):160. doi: 10.1186/s12916-020-01640-8 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 4. Alfaro T, Martinez‐Folgar K, Vives A, Bilal U. Excess mortality during the COVID‐19 pandemic in cities of Chile: magnitude, inequalities, and urban determinants. J Urban Health. 2022;99(5):922‐935. doi: 10.1007/s11524-022-00658-y [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 5. Quinn SC, Kumar S. Health inequalities and infectious disease epidemics: a challenge for global health security. Biosecurity Bioterrorism Biodefense Strategy Pract Sci. 2014;12(5):263‐273. doi: 10.1089/bsp.2014.0032 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 6. Lönnroth K, Jaramillo E, Williams BG, Dye C, Raviglione M. Drivers of tuberculosis epidemics: The role of risk factors and social determinants. Soc Sci Med. 2009;68(12):2240‐2246. doi: 10.1016/j.socscimed.2009.03.041 [DOI] [PubMed] [Google Scholar]
  • 7. Tusting LS, Willey B, Lucas H, et al. Socioeconomic development as an intervention against malaria: a systematic review and meta‐analysis. The Lancet. 2013;382(9896):963‐972. doi: 10.1016/S0140-6736(13)60851-X [DOI] [PubMed] [Google Scholar]
  • 8. Hotez PJ, Kamath A. Neglected tropical diseases in Sub‐Saharan Africa: review of their prevalence, distribution, and disease burden. PLoS Negl Trop Dis. 2009;3(8):e412. doi: 10.1371/journal.pntd.0000412 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 9. Livingston G, Huntley J, Liu KY, et al. Dementia prevention, intervention, and care: 2024 report of the lancet standing commission. Lancet Lond Engl. 2024;404(10452):572‐628. doi: 10.1016/S0140-6736(24)01296-0 [DOI] [PubMed] [Google Scholar]
  • 10. Majoka MA, Schimming C. Effect of social determinants of health on cognition and risk of Alzheimer disease and related dementias. Clin Ther. 2021;43(6):922‐929. doi: 10.1016/j.clinthera.2021.05.005 [DOI] [PubMed] [Google Scholar]
  • 11. Adkins‐Jackson PB, George KM, Besser LM, et al. The structural and social determinants of Alzheimer's disease related dementias. Alzheimers Dement. 2023;19(7):3171‐3185. doi: 10.1002/alz.13027 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 12. Harper MI, McKinney K, McLennan C, et al. Advocates, Academics, Survivors and Clinicians to END Intimate Partner Violence (ASCEND‐IPV) initiative: a prospective observational case‐control study protocol to identify plasma biomarkers of intimate partner violence (IPV)‐caused brain injury (BI). BMJ Open. 2025;15(9):e098025. doi: 10.1136/bmjopen-2024-098025 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 13. Itzhaki RF. Overwhelming evidence for a major role for Herpes Simplex Virus Type 1 (HSV1) in Alzheimer's Disease (AD); underwhelming evidence against. Vaccines. 2021;9(6):679. doi: 10.3390/vaccines9060679 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 14. Muzambi R, Bhaskaran K, Brayne C, Davidson JA, Smeeth L, Warren‐Gash C. Common bacterial infections and risk of dementia or cognitive decline: a systematic review. J Alzheimer's Dis. 2020;76(4):1609‐1626. doi: 10.3233/JAD-200303 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 15. Peixoto VGMNP, Azevedo JP, Luz KG, Almondes KM. Cognitive dysfunction of chikungunya virus infection in older adults. Front Psychiatry. 2022;13:823218 doi: 10.3389/fpsyt.2022.823218 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 16. Camacho‐Soto A, Faust I, Laurido‐Soto OJ, et al. Risk of developing Alzheimer disease in relation to common infections. Neurodegener Dis. 2025;25(4):180‐188 Published online May 30, 2025:1‐9. doi: 10.1159/000546589 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 17. Eyting M, Xie M, Michalik F, Heß S, Chung S, Geldsetzer P. A natural experiment on the effect of herpes zoster vaccination on dementia. Nature. 2025;641(8062):438‐446. doi: 10.1038/s41586-025-08800-x [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 18. Pomirchy M, Bommer C, Pradella F, Michalik F, Peters R, Geldsetzer P. Herpes zoster vaccination and dementia occurrence. JAMA. 2025;333(23):2083‐2092. doi: 10.1001/jama.2025.5013 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 19. Sacre A, Bambra C, Wildman JM, et al. Socioeconomic inequalities in vaccine uptake: a global umbrella review. PLOS ONE. 2023;18(12):e0294688. doi: 10.1371/journal.pone.0294688 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 20. Dave P. The impact of social determinants of health on vaccination uptake. Asian J Dent Health Sci. 2024;4(2):61‐66. doi: 10.22270/ajdhs.v4i2.90 [DOI] [Google Scholar]
  • 21. Ye X, Wang Y, Zou Y, et al. Associations of socioeconomic status with infectious diseases mediated by lifestyle, environmental pollution and chronic comorbidities: a comprehensive evaluation based on UK Biobank. Infect Dis Poverty. 2023;12(1):5. doi: 10.1186/s40249-023-01056-5 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 22. Bayraktaroglu I, Ortí‐Casañ N, Van Dam D, De Deyn PP, Eisel ULM. Systemic inflammation as a central player in the initiation and development of Alzheimer's disease. Immun Ageing. 2025;22(1):33. doi: 10.1186/s12979-025-00529-5 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 23. Rangel GA, Muñoz BA, Ramirez M, et al. The association of specific and cumulative exposure to infectious agents with cognitive impairment in older Hispanic adults. J Alzheimers Dis Rep. 2025;9:25424823251361066. doi: 10.1177/25424823251361066 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 24. Dominy SS, Lynch C, Ermini F, et al. Porphyromonas gingivalis in Alzheimer's disease brains: evidence for disease causation and treatment with small‐molecule inhibitors. Sci Adv. 2019;5(1):eaau3333. doi: 10.1126/sciadv.aau3333 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 25. Shawkatova I, Durmanova V, Javor J. Alzheimer's disease and porphyromonas gingivalis: exploring the links. Life. 2025;15(1):96. doi: 10.3390/life15010096 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 26. Felici C, Green RE, Warren‐Gash C, et al. Associations of several common infections with amyloid‐β pathology in vivo: a population‐based study. Alzheimers Dement. 2024;20(S7):e092178. doi: 10.1002/alz.092178 [DOI] [Google Scholar]
  • 27. Cameron L, Chase C, Haque S, Joseph G, Pinto R, Wang Q. Childhood stunting and cognitive effects of water and sanitation in Indonesia. Econ Hum Biol. 2021;40:100944. doi: 10.1016/j.ehb.2020.100944 [DOI] [PubMed] [Google Scholar]
  • 28. Gaylord AP, Maphula A, Scharf RJ, et al. Associations between point‐of‐use water treatment interventions and cognitive scores among children 5 years of age and younger in Limpopo, South Africa. Am J Trop Med Hyg. 2025;113(5):1131‐1137. doi: 10.4269/ajtmh.25-0122 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 29. Camacho‐Soto A, Faust I, Laurido‐Soto OJ, et al. Risk of developing Alzheimer disease in relation to common infections. Neurodegener Dis. 2025;25(4):180‐188. Published online May 30, 2025. doi: 10.1159/000546589 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 30. Xu W, Yu JT, Tan MS, Tan L. Cognitive reserve and Alzheimer's disease. Mol Neurobiol. 2015;51(1):187‐208. doi: 10.1007/s12035-014-8720-y [DOI] [PubMed] [Google Scholar]
  • 31. Williams VJ, Trane R, Sicinski K, Herd P, Engelman M, Asthana S. Midlife and late‐life environmental exposures on dementia risk in the wisconsin longitudinal study: the modifying effects of ApoE. Alzheimers Dement. 2024;20(12):8263‐8278. doi: 10.1002/alz.14216 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 32. Besser LM, Edwards K, Lobban NS, Tolea MI, Galvin JE. Social determinants of health, risk and resilience against Alzheimer's disease and related dementias: the healthy brain initiative. J Alzheimers Dis Rep. 2024;8(1):637‐646. doi: 10.3233/ADR-230155 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 33. Lyttelton T, Zang E. Occupations and sickness‐related absences during the COVID‐19 pandemic. J Health Soc Behav. 2022;63(1):19‐36. doi: 10.1177/00221465211053615 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 34. Li JM, Boustani MA, French DD. Social determinants of health in community‐dwelling dementia patients aged 65 and over: analysis of the 2019 national health interview survey. Gerontol Geriatr Med. 2023;9:23337214231190244. doi: 10.1177/23337214231190244 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 35. Joshi P, Tampi R. Social determinants of health for Alzheimer's disease and other dementias. Psychiatr Ann. 2024;54(7):e216‐e222. doi: 10.3928/00485713-20240618-05 [DOI] [Google Scholar]
  • 36. Scarmeas N, Stern Y. Cognitive reserve and lifestyle. J Clin Exp Neuropsychol. 2003;25(5):625‐633. doi: 10.1076/jcen.25.5.625.14576 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 37. Padda I, Fabian D, Farid M, et al. Social determinants of health and its impact on cardiovascular disease in underserved populations: a critical review. Curr Probl Cardiol. 2024;49(3):102373. doi: 10.1016/j.cpcardiol.2024.102373 [DOI] [PubMed] [Google Scholar]
  • 38. Testai FD, Gorelick PB, Chuang PY, et al. Cardiac contributions to brain health: a scientific statement from the american heart association. Stroke. 2024;55(12):e425‐e438. doi: 10.1161/STR.0000000000000476 [DOI] [PubMed] [Google Scholar]
  • 39. Merk H, Kühlmann‐Berenzon S, Bexelius C, et al. The validity of self‐initiated, event‐driven infectious disease reporting in general population cohorts. PLOS ONE. 2013;8(4):e61644. doi: 10.1371/journal.pone.0061644 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 40. Frisse AC, Marrazzo JM, Tutlam NT, et al. Validity of self‐reported history of Chlamydia trachomatis infection. Am J Obstet Gynecol. 2017;216(4):393.e1‐393.e7. doi: 10.1016/j.ajog.2016.12.005 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 41. Yoneoka D, Kawashima T, Tanoue Y, Nomura S, Eguchi A. Estimation of the time of exposure based on interval and censored data using the ε‐accelerated EM algorithm. Stat Med. 2023;42(25):4542‐4555. doi: 10.1002/sim.9874 [DOI] [PubMed] [Google Scholar]
  • 42. Lim FJ, Blyth CC, Levy A, et al. Using record linkage to validate notification and laboratory data for a more accurate assessment of notifiable infectious diseases. BMC Med Inform Decis Mak. 2017;17(1):86. doi: 10.1186/s12911-017-0484-7 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 43. Muscatello DJ, Amin J, MacIntyre CR, et al. Inaccurate ascertainment of morbidity and mortality due to influenza in administrative databases: a population‐based record linkage study. PLOS ONE. 2014;9(5):e98446. doi: 10.1371/journal.pone.0098446 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 44. Simanek AM, Dowd JB, Pawelec G, Melzer D, Dutta A, Aiello AE. Seropositivity to cytomegalovirus, inflammation, all‐cause and cardiovascular disease‐related mortality in the United States. PLOS ONE. 2011;6(2):e16103. doi: 10.1371/journal.pone.0016103 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 45. Dowd JB, Palermo T, Brite J, McDade TW, Aiello A. Seroprevalence of Epstein‐Barr Virus Infection in U.S. children ages 6‐19, 2003‐2010. PLOS ONE. 2013;8(5):e64921. doi: 10.1371/journal.pone.0064921 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 46. Mitchell RE, Jones HJ, Yolken RH, et al. Longitudinal serological measures of common infection in the avon longitudinal study of parents and children cohort. Wellcome Open Res. 2018;3:49. doi: 10.12688/wellcomeopenres.14565.2 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 47. Sharath Kumar GG, Adiga CP, Iyer PP, Goolahally LN. Role of imaging in CNS infections. Indian J Pathol Microbiol. 2022;65(Suppl 1):S153. doi: 10.4103/ijpm.ijpm_1162_21 [DOI] [PubMed] [Google Scholar]
  • 48. Eldin C, Parola P, Raoult D. Limitations of diagnostic tests for bacterial infections. Médecine Mal Infect. 2019;49(2):98‐101. doi: 10.1016/j.medmal.2018.12.004 [DOI] [PubMed] [Google Scholar]
  • 49. Tilocca B, Pieroni L, Soggiu A, et al. Gut‐brain axis and neurodegeneration: state‐of‐the‐art of meta‐omics sciences for microbiota characterization. Int J Mol Sci. 2020;21(11):4045. doi: 10.3390/ijms21114045 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 50. Zhao H, Zhou X, Song Y, et al. Multi‐omics analyses identify gut microbiota‐fecal metabolites‐brain‐cognition pathways in the Alzheimer's disease continuum. Alzheimers Res Ther. 2025;17(1):36. doi: 10.1186/s13195-025-01683-0 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 51. Baião AR, Cai Z, Poulos RC, et al. A technical review of multi‐omics data integration methods: from classical statistical to deep generative approaches. Brief Bioinform. 2025;26(4):bbaf355. doi: 10.1093/bib/bbaf355 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 52. Alemu R, Sharew NT, Arsano YY, et al. Multi‐omics approaches for understanding gene‐environment interactions in noncommunicable diseases: techniques, translation, and equity issues. Hum Genomics. 2025;19(1):8. doi: 10.1186/s40246-025-00718-9 [DOI] [PMC free article] [PubMed] [Google Scholar]

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